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Record W2118731101 · doi:10.1139/x05-182

Does thinning intensity affect the tracheid dimensions of Norway spruce?

2005· article· en· W2118731101 on OpenAlexvenueno aff
Tuula Jaakkola, Harri Mäkinen, Matti-P. Sarén, Pekka Saranpää

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsTracheidThinningPicea abiesIntensity (physics)HorticultureBotanyGrowth rateBiologyXylemMathematicsEcologyGeometryPhysics

Abstract

fetched live from OpenAlex

The effect of thinning intensity on the growth rate and tracheid dimensions of Norway spruce (Picea abies (L.) Karst) was studied in two long-term thinning experiments (Heinola and Punkaharju) in southeastern Finland. The stand age was 86 and 67 years in Heinola and Punkaharju, respectively. Thinning intensities in this study were lower and higher than recommended in the 1960s for forestry practice in Finland. An increase in tree growth rate (31% in Heinola and 37% in Punkaharju) caused by the high thinning intensity resulted in slightly shorter tracheids (9% in Heinola and 4% in Punkaharju) than with the low thinning intensity. Increased growth rate had no pronounced effect on tracheid cell wall thickness and lumen diameter. A faster growth rate slightly decreased the average cell wall thickness of an annual ring, but the changes in average lumen diameter were small. The effect of thinning intensity was similar in earlywood and latewood. Variation in fiber properties between and within individual trees and annual rings was large. In conclusion, the current thinning intensities used in Finnish forestry practice enhance growth rate but have a rather small effect on tracheid dimensions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.285
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2005
Admission routes1
Has abstractyes

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